This thesis concentrates on comparing novel and existing dereverberation and speaker
separation techniques using multiple corpora, including a new corpus collected using
a microphone array. Many corpora currently used for these techniques are recorded
using head-mounted microphones in anechoic chambers. This novel corpus contains
recordings with noise and reverberation made in office and workshop environments.
Novel algorithms present a different way of approximating the reverberation, producing results that are competitive with existing algorithms.
Dereverberation is evaluated using seven correlation-based algorithms and applied to two different corpora. Three of these are novel algorithms (Hs NTF, Cauchy
WPE and Cauchy MIMO WPE). Both non-learning and learning algorithms are
tested, with the learning algorithms performing better.
For single and multi-channel speaker separation, unsupervised non-negative matrix factorization (NMF) algorithms are compared using three cost functions combined with sparsity, convolution and direction of arrival. The results show that the
choice of cost function is important for improving the separation result. Furthermore, six different supervised deep learning algorithms are applied to single channel
speaker separation. Historic information improves the result. When comparing
NMF to deep learning, NMF is able to converge faster to a solution and provides a
better result for the corpora used in this thesis
